Sunday, March 22, 2026

"It Depends" Might be the Answer to the Question of "Can We Reduce Workweeks to 4 Days"

Some tests of a four-day workweek suggest output can be maintained, compared to a five-day workweek, at least in a test. Some skeptics might wonder whether the stable output, 20 percent less work time pattern can be maintained in a permanent, post-test environment, as studies in the past have shown that output improves when people are in a test situation. 


That Hawthorne effect refers to a behavioral observation that people change (usually improve) their performance or behavior simply because they know they are being observed, studied, or given special attention, and not because of the actual intervention being tested. 


For example, medical personnel hand washing increased 55 percent when personnel were in a test of hand washing. 


The point is that we cannot generalize from the results of limited-time tests because there is a significant possibility that output or behavior actually improves simply because people know they are being watched and evaluated. 


The principle was first discovered in the Hawthorne Works experiments (1924–1932) at a Western Electric factory in Cicero, Illinois. Researchers tested variables such as lighting levels, rest breaks, shorter days, and work structure changes on relay-assembly workers. 


Productivity rose in almost every condition (even when lighting was dimmed), and it often fell back to baseline once the study ended. 


Analysts later concluded the gains stemmed from the workers feeling noticed by researchers and supervisors, forming a cohesive “special” group, receiving sympathetic oversight, and the sheer novelty of being part of an experiment.


And that is the issue: it is not clear that a permanent four-day workweek would permanently sustain the “same productivity or output” as a five-day week, as we know that awareness of scrutiny can temporarily boost output, temporarily. 


We might also argue that employee well being is enhanced, but that productivity might not be similarly improved. It is hard to evaluate the productivity implications of “burnout,” for example. Many of us might note casually that many workers do not seem to produce as efficiently on Friday as at the beginning of the workweek, for example. 


In other words, the marginal output on day five might be low enough that a shorter workweek might possibly not damage overall productivity very much. 


There also is the Pygmalion Effect (also called the Rosenthal effect or teacher expectancy effect), a psychological phenomenon where higher expectations from one person (a leader, teacher, or supervisor) lead to improved performance in another through a self-fulfilling prophecy. 


It operates via subtle behavioral changes: when expectations are raised, the "expecter" provides more support, opportunities, feedback, encouragement, and trust; the "expectee" internalizes this, boosts self-efficacy and motivation, expends greater effort, and ultimately performs better, often confirming the original high expectations.


The Hawthorne Effect is a legitimate reason to view short-term pilot results with healthy skepticism. On the other hand, accumulating evidence from longer follow-ups, real-world scaling (Iceland), and high continuation rates indicates that genuine efficiency gains, reduced fatigue, and better focus from the shorter schedule are also at work. 


One might argue that if output over a four-day week remains at least the same as was seen with a five-day week, then the prior level was substantially suboptimal and inefficient, involving waste, fatigue-driven drag, and diminishing (or even negative) marginal returns on the fifth day or later hours.


The law of diminishing marginal returns applies to labor hours just as to other inputs. Adding hours beyond 35 to 48 per week (context-dependent) yields progressively less additional output per hour, as fatigue, errors, and disengagement rise. 


The point is that longer working hours might lead to output-per-hour falling as total hours climb. 


In trials, workers might have achieved the same (or more) total output in 20 percent less time by becoming more focused and rested. Also, less procrastination, fewer unnecessary meetings, and less "performative" busyness might also have occurred. 


In that view, the fifth day wasn't "producing" proportionally; it was often recovery from the first four. Hidden costs (burnout, sick days, turnover) made the five-day model less efficient societally. In other words, we weren't as productive as we thought, we were just working more.


Long-term, recovered workers sustain higher innovation and lower error rates.


Of course, in some cases there is an opportunity cost. In theory, that fifth day could fund deep work, skill-building, research or development collaboration, or innovation sprints that compound future output far beyond maintenance. 


A hyper-optimized culture might squeeze even higher total production by treating every hour as investment rather than default leisure.


Much could hinge on the type of firm; the outputs; employee commitment and other inputs. Dysfunctional firms are unlikely to suddenly be transformed into high-performance firms simply by reducing hours. 


It might be the case that ‘working smarter” outperforms grinding longer, at least for firms that are well run; have high worker buy-in; good management; are highly dependent on creativity and have other hallmarks of high-performing organizations.


There are likely meaningful differences in how a four-day workweek affects different types of firms. 


Creativity or insight-dependent organizations such as R&D labs, research institutes, artistic/creative agencies, design firms, innovative tech/product development might benefit more directly. 


Firms whose output is standardized or routinized, such as customer service contact centers, routine manufacturing or assembly, standardized professional services like basic admin or call handling, retail operations, or shift-based public services like certain nursing or police functions might be more likely to see lower output, as they are a function of “hours available.”


The point is, there might not be one universal answer to the question “can we reduce workweeks from five to four and still maintain present output.”


Saturday, March 21, 2026

Anthropic Survey Finds 81% of Respondents Think AI is Creating Value

After surveying 80,508 people across 159 countries and 70 languages, here is how Anthropic assesses the hopes people have for artificial intelligence. The survey asked:

  • what people want from AI

  • whether they’re getting what they want

  • what they fear

  • what they do for a living

  • sentiment about AI overall.


Roughly a third of visions are about making room for life (more time, money, mental bandwidth). About a quarter revolve around using AI to help people do better, more fulfilling work.


About a fifth of responses are about becoming someone better (learning, healing, growing). 


A smaller share want to make something (“creative expression”) or fix the world (“societal transformation”). Those that wanted societal transformation from AI often cited a vision for healthcare. People wanted AI to detect cancer earlier, accelerate drug discovery, or enable broad access.


Respondents in low and middle income countries were quick to cite the possibility that AI might break the association between educational quality and wealth.


Some of you might be encouraged: these are normal, human hopes for new technology. 


source: Anthropic 


And even if user beliefs about impact are subjective (people might think an innovation has one impact; it might not), the survey suggests people believe AI is having an impact.


source: Anthropic 


About 81 percent of respondents said AI already has helped them fulfill their own stated visions for benefit. 


Granted, those are subjective responses, and far from the quantifiable outcomes investors ultimately will expect. But in the near term, we will have to keep looking for value proxies.


"Not Seeing AI Productivity" Storyline is Inevitable

It’s inevitable that we will keep seeing headlines, and seeing, hearing and reading stories about how many businesses are not seeing financial returns from their investments in artificial intelligence


Important new technologies rarely show up in the bottom line immediately, and the issues are structural.


First of all, business processes have to be recreated to harness the innovations. 


When electricity first entered factories, managers simply replaced their massive central steam engine with one massive electric motor. Productivity didn't move. Only after firms discovered they could put a small motor on every individual machine (the "unit drive") were they able to redesign the factory floor. 


In 2026 companies are using AI to "chat with docs" or "summarize emails" (overlaying tech on old habits) rather than redesigning their entire supply chains. That will take some time. 


Also, firms must retrain workers and staffs. That imposes real costs (time and money) while possibly lowering productivity in the short run as time and effort is diverted to such training. So a "J-curve" of productivity will happen: lower productivity in the near term, with the benefits in the future. .


Then there are measurement issues, such as how to quantify the impact of quality, variety and speed. If an AI helps a legal team finish a contract in two hours instead of 10, but the firm still charges a flat fee, the "productivity" is invisible to the GDP, even though the human cost has plummeted.


Study

Technology Period

Key Finding

Duration of Lag

Paul David (1990)

Electricity (1890–1920)

Factories had to be physically demolished and rebuilt to utilize "unit drive" motors before TFP spiked.

~30–40 Years

Robert Solow (1987)

Computing (1970–1990)

The "Solow Paradox": You could see the computer age everywhere except in the productivity statistics.

~20 Years

Brynjolfsson et al. (2021)

AI & Software (2010–2021)

Formulated the "Productivity J-Curve"; firms must invest in unmeasured "intangible assets" that initially depress earnings.

Ongoing

NBER / Juhász et al. (2020)

19th Century France

Productivity in mechanized spinning was initially lower and more dispersed than hand-spinning due to the need for factory reorganization.

~15–20 Years

Man Group / Bara (2026)

Generative AI (2023–2026)

80% of firms report no macro productivity impact yet, despite task-level gains of 15-55%, due to "workflow friction."

Projected 5-10 Years


In the meantime, leaders will have to try and come up with some quantifiable metrics (directly related or not) to justify the investments. It won’t take too much imagination to realize that headcount reductions are one such way to “show” outcomes, even if AI and headcount are indirectly, loosely or even unrelated in the short term. 


In 1900 the "electricity bubble" looked real to everyone still using steam. By 1920, the steam users were bankrupt. 


So “productivity proxies” must be developed.


The most immediate impact of AI is the compression of time. 


Firms can measure the "distance" between an idea and its execution.Time-to-prototype can show how many days it takes to move from a natural language prompt or requirement to a functional, testable version.


Draft-to-final ratio might be used by marketing and legal firms to measure the time spent on the "first 80 percent" of a task versus the "final 20 percent" of human polishing.


For engineering teams, the metric isn't just "lines of code," but the number of successful pushes to production per developer per week. 


Larger firms might try to assess the reduction in total "human hours" spent in meetings.


Query-to-find latency is a measurement of how long it takes an employee to retrieve a specific piece of internal tribal knowledge. AI should reduce that latency. 


Admin-to-maker ratio tracks whether the percentage of an employee's day spent on "coordination" is shrinking in favor of "creation." 


“Agents” also will need new metrics that quantify AI outcomes as though it were a digital employee rather than a software tool.


Autonomous completion rate is the percentage of workflows that an AI agent initiates and completes without a human "click" or intervention.


Human-in-the-loop friction measures how often an agent has to "hand back" a task to a human because it hit a reasoning wall. A falling HITL rate is a leading indicator of future productivity.


Token efficiency per outcome calculates the cost of AI "thinking" (API/Compute costs) relative to a successful business outcome. 



Business Function

Traditional Metric (Lagging)

AI Proxy Metric (Leading)

Why it Matters

Software Engineering

Lines of Code, Story Points

PR Cycle Time

Measures how fast code is reviewed and merged, not just typed.

Legal, Compliance

Billable Hours

Review Velocity per Page

Shows the acceleration of document ingestion and risk flagging.

Customer Support

First Response Time

Resolution via Zero-Touch

Measures the percentage of issues solved entirely by agents.

R&D

Patents Filed, Products Launched

Iteration Cycles per Quarter

Shows how many "failed fast" experiments the firm can run.

Human Resources

Headcount Growth

Talent Density (Revenue/FTE)

Measures if the firm is scaling output without scaling people.


The “productivity lag” is entirely predictable. So are the storylines about it. Sure, it is a significant practical problem for those firms making the investments. But the “lag” storyline is entirely predictable.


Thursday, March 19, 2026

Outcomes Matter, Not Virtue Signaling

Adam Garfinkle's book Telltale Hearts argues that the U.S. antiwar movement of the 1960s (yes, Baby Boomers) did not meaningfully shorten the Vietnam War and may actually have prolonged it. 


That matters if you think it is more important to “do good” than to “feel good;” better to accomplish a change than simply to “virtue signal.” 


The attack is upon the  narrative, arguably central to Boomer self-understanding, that their activism decisively “ended the war.” He argues that story is emotionally satisfying but incorrect. 


For a generation that prides itself on being “transformational,” that puncturing of a myth might be uncomfortable, but a useful antidote to ingrained arrogance


Oddly enough, Garfinkle argues, both opponents of the war and those who believe it might actually have been won by the United States seem to agree on the movement’s impact. But both sides might be wrong. 


Garfinkle challenges the widespread belief that protests forced U.S. withdrawal and instead argues the movement had “marginal impact” (and maybe almost none) on ending the war. 


In fact, he says the movement actually was counterproductive:

  • provoked backlash

  • strengthened hardline positions

  • disrupted conventional political processes that might otherwise have constrained the war.


His most startling argument is that the protests might actually have extended the conflict and increased casualties. 


And other authors have made similar claims about a generation that might have created as many problems as it believes it solved:

  • Boomers: The Men and Women Who Promised Freedom and Delivered Disaster – Helen Andrews
    Argues Boomer elites reshaped institutions (media, politics, religion) in ways that produced long-term dysfunction

  • A Generation of Sociopaths – Bruce Cannon Gibney
    A blunt critique claiming Boomers extracted economic and social value while leaving debt and institutional decay

  • The Narcissism Epidemic – Jean Twenge
    Connects Boomer-era cultural shifts to rising individualism and narcissism (though broader than just Boomers).

Garfinkle’s work is narrower (focused on Vietnam), but:

  • Challenges moral self-congratulation

  • Highlights unintended consequences

  • Separates cultural impact from policy impact (huge in one, limited in the other)

Many will argue boomers were enormously influential. But influence is not the same as positive outcomes.


I may be a boomer, but I do not buy the self-congratulatory plaudits. Perhaps we meant well. But what matters are outcomes, not feelings.


Boomer economic impact likely is mixed, at best.


Author

Positive Effects

Negative Effects

Net View

Bruce Cannon Gibney (A Generation of Sociopaths)

Asset inflation, entitlement expansion, public debt burden shifted to younger generations

Strongly negative

Helen Andrews (Boomers)

Some institutional dynamism

Mismanagement of institutions, short-termism

Mostly negative

William Strauss & Neil Howe (Generations, The Fourth Turning)

Innovation, growth cycles

Fiscal imbalances, intergenerational strain

Cyclical / mixed


Boomer political or institutional impact might be a mix of positive and negative. 


Author

Positive Effects

Negative Effects

Net View

Adam Garfinkle (Telltale Hearts)

Raised awareness of war

Undermined political cohesion; limited policy effectiveness; possible prolongation of Vietnam War

Negative

Todd Gitlin (The Sixties)

Expanded democratic participation

Fragmentation, radicalization weakened movements

Mixed

Alan Wolfe (One Nation, After All)

Greater tolerance, pluralism

Decline in shared moral frameworks

Tradeoff


Cultural or social impact might be the most-questionable area of influence. 


Author

Positive Effects

Negative Effects

Net View

Jean Twenge (The Narcissism Epidemic)

Self-expression, individual empowerment

Rising narcissism, fragility, decline in social cohesion

Negative

Todd Gitlin

Liberation movements, civil rights gains

Excess, identity fragmentation

Mixed

Daniel Bell

Cultural creativity

Breakdown of norms supporting institutions

Tradeoff

Alan Wolfe

Tolerance, reduced prejudice

Moral relativism, weaker shared norms

Tradeoff


"It Depends" Might be the Answer to the Question of "Can We Reduce Workweeks to 4 Days"

Some tests of a four-day workweek suggest output can be maintained, compared to a five-day workweek, at least in a test. Some skeptics mig...